Although resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional gradients have been widely used to describe cortical hierarchical organization, their application has rarely targeted acute sleep deprivation (ASD)-related cognitive vulnerability. In particular, whether ASD induces systematic reorganization of gradient architecture and whether this reorganization contributes to spatial working memory (SWM) impairment have not yet been systematically examined. Fifty healthy young adult males were recruited. A 1-back task was administered to assess SWM performance before and after ASD. T1-weighted and rs-fMRI data were acquired. Functional gradient-based metrics, including standard deviation of gradient values, range of gradient values, network gradient values, and inter-network relative distances, were computed to characterize cortical hierarchical organization and were subsequently correlated with SWM behavioral performance. Compared with the rested wakefulness condition, ASD significantly impaired SWM performance. Functional gradient analysis revealed significant alterations in both global (standard deviation and range) and local (gradient values of specific subnetworks) features of the top three principal gradients. Notably, the standard deviation of Gradient 2 was significantly negatively correlated with omission rate. In addition, relative distances between multiple networks within Gradient 2 and 3 were also closely associated with SWM performance. From the perspective of functional gradients, the present study highlights the global and local gradient reorganization following ASD, as well as the importance of maintaining a balance between functional segregation and integration across subnetworks in sustaining SWM performance. • This study integrated gradient analysis and behavioral assessment to investigate how ASD affects brain functional organization and SWM. The results showed that ASD reshaped gradient distributions and was associated with SWM. Inter-network distances were associated with SWM, underscoring the role of balanced functional segregation and integration in supporting SWM.
Xu et al. (Sun,) studied this question.